%
% File naaclhlt2009.tex
%
% Contact: nasmith@cs.cmu.edu

\documentclass[11pt]{article}
\usepackage{naaclhlt2009}
\usepackage{times}
\usepackage{latexsym}
\usepackage[round]{natbib} 
\usepackage{amsmath}
\usepackage{amssymb} 
\usepackage[pdftex]{graphicx} 
\usepackage{hyperref} 
\usepackage{multirow}

\renewcommand{\baselinestretch}{.982} 

\setlength\titlebox{6.5cm}    % Expanding the titlebox

\title{Jointly Identifying Predicates, Arguments and Senses using Markov
Logic}

% \author{
% Ivan Meza-Ruiz$^{1}$ \\ {\tt I.V.Meza-Ruiz@sms.ed.ac.uk} \And Sebastian Riedel$^{2,3}$ \\ {\tt sebastian.riedel@gmail.com} \\
% ${^1}$ {School of Informatics, University of Edinburgh, UK}\\
% ${^2}$ {Department of Computer Science, University of Tokyo, Japan}\\
% ${^3}$ {Database Center for Life Science, Research Organization of Information and System, Japan}\\
% }

\author{
Ivan Meza-Ruiz\footnotemark[1]  \qquad Sebastian Riedel\footnotemark[2] \footnotemark[3]   \\
\footnotemark[1]  {School of Informatics, University of Edinburgh, UK}\\
\footnotemark[2]  {Department of Computer Science, University of Tokyo, Japan}\\
\footnotemark[3]  {Database Center for Life Science, Research Organization of Information and System, Japan}\\
\footnotemark[1]  \tt  I.V.Meza-Ruiz@sms.ed.ac.uk \footnotemark[2] \tt sebastian.riedel@gmail.com
}


% \author{Takashi Tsunakawa\footnotemark[2] \qquad Naoaki Okazaki\footnotemark[2] \qquad Jun'ichi Tsujii\footnotemark[2] \footnotemark[3] \\
%   \footnotemark[2] Department of Computer Science, \\
%   Graduate School of Information Science and Technology, University of Tokyo \\
%   7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033 Japan \\
%   \footnotemark[3] School of Computer Science, University of Manchester / National Centre for Text Mining \\
%   131 Princess Street, Manchester, M1 7DN, UK \\
%   {\tt \{tuna, okazaki, tsujii\}@is.s.u-tokyo.ac.jp}}


%SR: Fancy double affiliation for me :)

% \author{Ivan Meza-Ruiz \\
%   School of Informatics\\
%   University of Edinburgh, UK\\
%   {\tt I.V.Meza-Ruiz@sms.ed.ac.uk} \And
%   Sebastian Riedel\\
%   University of Tokio, Japan\\
%   {\tt S.R.Riedel@sms.ed.ac.uk}
%   }



\begin{document}

% Remember to aknowledge Mihai


\maketitle
\input{macros.tex}
\begin{abstract}
In this paper we present a Markov Logic Network for Semantic Role
Labelling that jointly performs predicate identification, frame
disambiguation, argument identification and argument classification
for all predicates in a sentence. Empirically we find that our
approach is competitive: our best model would appear on par
with the best entry in the CoNLL 2008 shared task open track, and at
the 4th place of the closed track---right behind the systems that use
significantly better parsers to generate their input features.
%IV-
%In contrast, our model uses the vanilla MALT
%parses provided by the shared task organizers. 
Moreover, we observe
that by fully capturing the complete SRL pipeline in a single
probabilistic model we can achieve significant improvements over more isolated systems, in particular for out-of-domain
data. Finally, we show that despite the joint approach, our
system is still efficient. 
\end{abstract} 


\section{Introduction}


\input{introduction.tex}

\section{Markov Logic} \label{sec:markovlogic}

\input{markovlogic.tex}

\section{Model} \label{sec:model} 

\input{model.tex}

\section{Inference and Learning}\label{sec:inference}

\input{inference}


\section{Experimental Setup}
\label{sec:experiments}
\input{experiments.tex}

\section{Results}\label{sec:results}

\input{results.tex}

\section{Conclusion} \label{sec:conclusion}

\input{conclusion.tex}

\section*{Acknowledgements}

The authors are grateful to Mihai Surdeanu for providing the version of
the corpus used in this work.

\bibliographystyle{plainnat}
\bibliography{seb}


\end{document}

% Comments
%---
% Reviewer 1
%---
% 1. error analysis can be very difficul, discussed.
%    IV. Mention this ??
% 2. The natural question, why the authors didn't switch parsers.
%    IV. Dogde this ball, for now.
% 3. Statistical signficant.
%    IV. Maybe do this
% 4. Number of assigments
%    IV. ?? No idea what's this.
% 5. Pipe-line -> other systems.
%    IV. Make clear this is seb's implementation (include beast links and models)
% 6. Equation (1), w is the same as w-sub-phii next line? 
%    I think it should be the same, check with Seb
% 7. Phi Subscript for (phi,w), is it right?
%    I think it is right
% 8. primes x' and take'.
%    Erase the simple quotes (Done)
% 9. Figure 2, functor w?
%    Figure says rectangles are formulae and functors. We need to clarify this.
%    (done)
% 10. Figure 2, subscript a, rather than a simple index.
%    Change the subscripts.
% 11. Explanation of '+'
%     Imply that weights have indeces, rather than what means. (done)
% 12. End of Section 1.  Second reference to section 3 looks like it should be to
%     section 4.
 
 
%---
% Reviewer 2
%---
% 1. For comparison purposes, are better parses available?
%    IV. Dodge the ball.
% 2. If so, would using them result in significantly different results?
%    IV. Maybe hint this happens, otherwise dodge the ball.
% 3. high-level description of previously published inference methods -
%    I'd suggest either including some lines of pseudocode or just including the pointers to the previous work - right now.
% 4. which contains not just an interpreter but also a number of inference algorithms ?)
%    Alchemy

%---
% Reviewer 3
%============================================================================
                            %REVIEWER #3
%============================================================================ 

% 1. little more detail in the particulars of the MLN training process,i
% 2. how the data was prepared in order for used as input.

